通过feedforward神经网络获得的单个焦点的通用二元螺旋区域板.
Optics express
|September 15, 2023
概括
使用人工神经网络设计的通用二元螺旋区域板 (GBSZPs) 有效地消除不需要的焦点. 在光学生成的这种进步为改进的光学图像处理和量子计算提供了一个单一的焦点.
科学领域:
- 光学和光子学 在光学和光子学.
- 在光学领域的人工智能.
背景情况:
- 传统的螺旋带板 (SZP) 产生光学,但存在多个焦点,限制了应用.
- 消除高阶焦点的现有方法涉及具有正弦传导率的二进制结构,这并不总是最佳的.
研究的目的:
- 提出和设计能够产生单个焦点的通用双螺旋区域板 (GBSZPs).
- 利用前神经网络 (FNN) 来设计具有被抑制高阶焦点的GBSZPs.
主要方法:
- 概括二元螺旋区域板 (GBSZPs) 的概念开发.
- 训练一个前神经网络 (FNN) 来映射结构参数以聚焦强度.
- 使用训练有素的FNN预测单焦发电的GBSZP结构参数.
主要成果:
- 模拟和实验表明,GBSZPs可以将高阶焦点的强度降低到第一阶焦点的0.2%.
- 优化的GBSZP结构表现出与传统的侧面形状不同的辐射传导功能的功能.
- 这表明非形辐射型也可以有效地消除高阶焦点.
结论:
- 通过FNNs设计的拟议的GBSZPs提供了一种创新的方法来产生单焦光学.
- 这些发现为设计具有性能改进的二进制区域板提供了新的方向.
- 这项研究对推进光学图像处理和量子计算有潜在的影响.
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